code: def test_transformed_distribution(
      base_batch_dim,
      base_event_dim,
      transform_dim,
      num_transforms,
      sample_shape,
  ):
      shape = torch.Size([2, 3, 4, 5])
      base_dist = Normal(0, 1)
      base_dist = base_dist.expand(
          shape[4 - base_batch_dim - base_event_dim :]
      )
      if base_event_dim:
          base_dist = Independent(base_dist, base_event_dim)
      transforms = [
          AffineTransform(torch.zeros(shape[4 - transform_dim :]), 1),
          ReshapeTransform((4, 5), (20,)),
          ReshapeTransform((3, 20), (6, 10)),
      ]
      transforms = transforms[:num_transforms]
      transform = ComposeTransform(transforms)
  
      # Check validation in .__init__().
      if base_batch_dim + base_event_dim < transform.domain.event_dim:
          with pytest.raises(ValueError):
              TransformedDistribution(base_dist, transforms)
          return
      d = TransformedDistribution(base_dist, transforms)
  
      # Check sampling is sufficiently expanded.
      x = d.sample(sample_shape)
      assert x.shape == sample_shape + d.batch_shape + d.event_shape
      num_unique = len(set(x.reshape(-1).tolist()))
      assert num_unique >= 0.9 * x.numel()
  
      # Check log_prob shape on full samples.
      log_prob = d.log_prob(x)
      assert log_prob.shape == sample_shape + d.batch_shape
  
      # Check log_prob shape on partial samples.
      y = x
      while y.dim() > len(d.event_shape):
          y = y[0]
      log_prob = d.log_prob(y)
      assert log_prob.shape == d.batch_shape
  
imports: import io
  from numbers import Number
  import pytest
  import torch
  from torch.autograd import grad
  from torch.autograd.functional import jacobian
  from torch.distributions import Dirichlet, Independent, Normal, TransformedDistribution, constraints
  from torch.distributions.transforms import AbsTransform, AffineTransform, ComposeTransform, CorrCholeskyTransform, CumulativeDistributionTransform, ExpTransform, IndependentTransform, LowerCholeskyTransform, PowerTransform, ReshapeTransform, SigmoidTransform, TanhTransform, SoftmaxTransform, SoftplusTransform, StickBreakingTransform, identity_transform, Transform, _InverseTransform, PositiveDefiniteTransform
  from torch.distributions.utils import tril_matrix_to_vec, vec_to_tril_matrix
  from torch.testing._internal.common_utils import run_tests
label_desc: Use image node_id 17 for calling a global function with example usage: test_transformed_distribution(base_batch_dim, base_event_dim, transform_dim, num_transforms, sample_shape) without return types